Detecting patterns of climate change in long-term forecasts of marine environmental parameters |
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Authors: | Gianpaolo Coro Pasquale Pagano Anton Ellenbroek |
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Institution: | 1. Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo” – CNR, Pisa, Italycoro@isti.cnr.ithttps://orcid.org/0000-0001-7232-191X;3. Istituto di Scienza e Tecnologie dell’Informazione “A. Faedo” – CNR, Pisa, Italy;4. Food and Agriculture Organization of the United Nations (FAO), Rome, Italy |
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Abstract: | ABSTRACTForecasting environmental parameters in the distant future requires complex modelling and large computational resources. Due to the sensitivity and complexity of forecast models, long-term parameter forecasts (e.g. up to 2100) are uncommon and only produced by a few organisations, in heterogeneous formats and based on different assumptions of greenhouse gases emissions. However, data mining techniques can be used to coerce the data to a uniform time and spatial representation, which facilitates their use in many applications. In this paper, streams of big data coming from AquaMaps and NASA collections of 126 long-term forecasts of nine types of environmental parameters are processed through a cloud computing platform in order to (i) standardise and harmonise the data representations, (ii) produce intermediate scenarios and new informative parameters, and (iii) align all sets on a common time and spatial resolution. Time series cross-correlation applied to these aligned datasets reveals patterns of climate change and similarities between parameter trends in 10 marine areas. Our results highlight that (i) the Mediterranean Sea may have a standalone ‘response’ to climate change with respect to other areas, (ii) the Poles are most representative of global forecasted change, and (iii) the trends are generally alarming for most oceans. |
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Keywords: | Climate change environmental parameters forecasting environmental parameters time series ecological modelling species distribution modelling AquaMaps NASA Earth Exchange |
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